docstr: Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
  args:
      x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
      incremental_state: A dict to keep the state
docstr_tok: ["Assuming","the","input",",","x",",","of","the","shape","T","x","B","x","C","and","producing","an","output","in","the","shape","T","x","B","x","C","args",":","x",":","Input","of","shape","T","x","B","x","C",",","i.e",".","(","timesteps",",","batch_size",",","input_size",")","incremental_state",":","A","dict","to","keep","the","state"]
code: def forward(
      self, x, incremental_state: Optional[Dict[str, Tensor]] = None
  ):
      """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
      args:
          x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
          incremental_state: A dict to keep the state
      """
  
      output = self._forward_unfolded(x, incremental_state)
  
      if self.has_bias:
          output = output + self.bias
      return output
  
file_path: pytext/pytext/models/seq_models/light_conv.py
imports: from typing import Dict, Optional
  import torch.jit
  import torch.nn
  from pytext.config import ConfigBase
  from torch import Tensor
  from .base import PyTextIncrementalDecoderComponent
  from .utils import unfold1d
label_desc: Use image node_id 4 for calling the LightweightConv obj's underlying member method code with example usage: obj.forward(x, incremental_state) and returns: output